Online data-driven adaptive control for unknown linear time-varying systems
File(s)
Accepted version
Author(s)
Liu, Shenyu
Chen, Kaiwen
Eising, Jaap
Type
Conference Paper
Abstract
This paper proposes a novel online data-driven adaptive control for discrete-time unknown linear time-varying systems. Initialized with an empirical feedback gain, the algorithm periodically updates this gain based on the data collected over a short time window before each update. Meanwhile, the stability of the closed-loop system is analyzed in detail, which shows that under some mild assumptions, the proposed online data-driven adaptive control scheme can guarantee practical global exponential stability. Finally, the proposed algorithm is demonstrated by numerical simulations and its performance is compared with other control algorithms for unknown linear time-varying systems.
Date Issued
2024-01-19
Date Acceptance
2023-12-01
Citation
2023 62nd IEEE Conference on Decision and Control (CDC), 2024, pp.8775-8780
ISSN
0743-1546
Publisher
IEEE
Start Page
8775
End Page
8780
Journal / Book Title
2023 62nd IEEE Conference on Decision and Control (CDC)
Copyright Statement
Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
2023 62nd IEEE Conference on Decision and Control (CDC)
Publication Status
Published
Start Date
2023-12-13
Finish Date
2023-12-15
Coverage Spatial
Singapore, Singapore